A 0.67 $\mu$V-IIRN super-T$\Omega$-Z$_{IN}$ 17.5 $\mu$W/Ch Active Electrode With In-Channel Boosted CMRR for Distributed EEG Monitoring
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Bibliographic record
Abstract
We present the design, development, and experimental characterization of an active electrode (AE) IC for wearable ambulatory EEG recording. The proposed architecture features in-AE double common-mode (CM) rejection, making the recording's CMRR independent of typically-significant AE-to-AE gain variations. Thanks to being DC coupled and needless of chopper stabilization for flicker noise suppression, the architecture yields a super-T$\Omega$input impedance. Such a large input impedance makes the AE's CMRR practically immune to electrode-skin interface impedance variations across different recording channels, a critical feature for dry-electrode ambulatory systems. Signal quantization and serialization are also performed in-AE, which enables a distributed system in which all AEs use a single data bus for data/command communication to the backend module, thus significantly improving the system's scalability. Additionally, the presented AE hosts auxiliary modules for (i) detection of an unstable electrode-skin connection through continuous interface impedance monitoring, (ii) dynamic measurement and adjustment of input DC level, and (iii) a CM feedback loop for further CMRR enhancement. The article also presents the development of printed (extrusion) tattoo electrodes and their experimental characterization results with the proposed AE architecture. Besides bio-compatibility, low-cost, pattern flexibility, and quick fabrication process, the printed electrodes offer a very stable electrode-skin connection, conform to scalp shape, and exhibit consistent performance under various bending curvatures. Analog circuit blocks of the presented AE architecture are designed and fabricated using a standard 180 nm CMOS technology, and the$1\times \text{1.3}\,\text{mm}^{2}$IC is integrated with off-chip low-power digital modules on a PCB to form the AE. Our measurement results show a CMRR of 82.2 dB (at 60 Hz), amplification voltage gain of 52.8 dB, a bandwidth of 0.2–400 Hz,$\pm$500 mV input DC offset tolerance, An input impedance$> \text{1}\,T\Omega$, and 0.67$\mu$V$_{RMS}$integrated input referred noise (0.5–100 Hz), while consuming 17.5$\mu$W per channel. All auxiliary modules are tested experimentally, and the entire system is validated in-vivo, for both ECG and EEG recording.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it